301 lines
12 KiB
Python
301 lines
12 KiB
Python
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import time
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import os
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import glog as log
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import numpy as np
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import json
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import torch
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import torch.nn.functional as F
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from runners.runner import Runner
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from copy import deepcopy
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from optim_utils import init_optim
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from transformers.models.bart.configuration_bart import BartConfig
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from models.nextqa_bart import AVSDBart
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from time import time
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def tokenize(obj, tokenizer):
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if isinstance(obj, str):
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return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(obj))
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if isinstance(obj, dict):
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return dict((n, tokenize(o)) for n, o in obj.items())
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return list(tokenize(o) for o in obj)
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class NEXTQARunner(Runner):
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def __init__(self, config, tokenizer, vocab_size):
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super(NEXTQARunner, self).__init__(config)
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bart_config = BartConfig.from_json_file(self.config['bart_config'])
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self.model = AVSDBart.from_pretrained(
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'facebook/bart-{}'.format(self.config['bart_size']), config=bart_config)
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# Resize the embedding to match the vocab with additional special toks
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# This takes care of resizing weights of related parts of the network
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if vocab_size != bart_config.vocab_size:
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self.model.resize_token_embeddings(vocab_size)
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self.model.to(self.config['device'])
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if not self.config['generating']:
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self.optimizer, self.scheduler = init_optim(self.model, self.config)
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self.tokenizer = tokenizer
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def forward(self, batch):
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for key in batch:
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if isinstance(batch[key], torch.Tensor):
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batch[key] = batch[key].cuda()
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########################################################
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input_ids = batch['input_ids']
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video_place_holder_ids = batch['video_place_holder_ids']
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app_feats = batch['app_feats']
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mot_feats = batch['mot_feats']
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lm_labels = batch['lm_labels']
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input_mask = batch['input_mask']
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app_interval = batch['app_interval']
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mot_interval = batch['mot_interval']
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question_intervals = batch['question_intervals']
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vis_state_vector_idx = batch['vis_state_vector_idx']
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question_state_vector_idx = batch['question_state_vector_idx']
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########################################################
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bart_output = self.model(
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input_ids=input_ids,
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video_place_holder_ids=video_place_holder_ids,
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i3d_rgb=app_feats,
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i3d_flow=mot_feats,
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attention_mask=input_mask,
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labels=lm_labels,
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i3d_rgb_interval=app_interval,
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i3d_flow_interval=mot_interval,
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question_intervals=question_intervals,
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vis_state_vector_idx=vis_state_vector_idx,
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question_state_vector_idx=question_state_vector_idx,
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output_attentions=True,
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return_dict=True
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)
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output = {}
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if self.config['print_output']:
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logits = bart_output['logits']
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probs = F.softmax(logits, dim=-1)
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preds = torch.topk(probs, 1)[1].squeeze(-1)
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preds = preds.tolist()
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lm_labels_list = lm_labels[:, 1:].tolist()
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lm_labels_list = [[s for s in label if s != -1] for label in lm_labels_list]
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reponses = ''
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labels = ''
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for pred, label in zip(preds, lm_labels_list):
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reponses += self.tokenizer.decode(pred) + '\n'
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labels += self.tokenizer.decode(label) + '\n'
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output['reponses'] = reponses
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output['gt'] = labels
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gen_key = 'gen_loss (x{})'.format(self.config['gen_coeff'])
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gen_loss = bart_output['gen_loss']
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gen_loss = self.config['gen_coeff'] * gen_loss
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elbo_global_key = 'elbo_loss_global (x{})'.format(self.config['elbo_global_coeff'])
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if bart_output['elbo_loss_global'] is not None:
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elbo_global_loss = bart_output['elbo_loss_global']
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elbo_global_loss = self.config['elbo_global_coeff'] * elbo_global_loss
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else:
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elbo_global_loss = torch.tensor(0.0)
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elbo_local_key = 'elbo_loss_local (x{})'.format(self.config['elbo_local_coeff'])
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if bart_output['elbo_loss_local'] is not None:
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elbo_local_loss = bart_output['elbo_loss_local']
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elbo_local_loss = self.config['elbo_local_coeff'] * elbo_local_loss
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else:
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elbo_local_loss = torch.tensor(0.0)
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total_loss = gen_loss + elbo_global_loss + elbo_local_loss
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output['losses'] = {
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gen_key: gen_loss,
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elbo_local_key: elbo_local_loss,
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elbo_global_key: elbo_global_loss,
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'tot_loss': total_loss
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}
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del bart_output
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return output
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def generate(self, dataset, app_feats, mot_feats, tag, tokenizer, start_idx_gen, end_idx_gen, gen_subset_num=None):
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self.model.eval()
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results = {}
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app_sep, mot_sep, ph_token = tokenizer.convert_tokens_to_ids(
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['<s0>', '<s1>', '<place_holder>'])
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# Generate the repsonse for each round
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log.info('[INFO] Generating responses for {} samples'.format(len(dataset)))
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with torch.no_grad():
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counter = 0
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for idx in range(start_idx_gen, end_idx_gen):
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start_time = time()
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cur_sample = dataset.loc[idx]
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video_name, ques, ans, qid = str(cur_sample['video']), str(cur_sample['question']),\
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str(cur_sample['answer']), str(cur_sample['qid'])
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if video_name not in results:
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results[video_name] = {}
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input_ids = tokenize(ques, tokenizer)
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app_feat = app_feats[video_name]
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app_feat = torch.from_numpy(app_feat).type(torch.float32)
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mot_feat = mot_feats[video_name]
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mot_feat = torch.from_numpy(mot_feat).type(torch.float32)
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bos, eos, ques_state = self.tokenizer.convert_tokens_to_ids(['<s>', '</s>', '<s2>'])
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# Add state tokens
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input_ids.insert(0, ques_state)
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input_ids = torch.Tensor(input_ids).long()
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dummy = torch.ones((1, 16)) * ph_token
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video_place_holder_ids = torch.cat(
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[torch.ones((1, 1)) * app_sep, dummy,
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torch.ones((1, 1)) * mot_sep, dummy,
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], dim=-1).long()
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# Now we get the intervals of the visual input tokens
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# Here the interval do not change across the batch dimension
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app_interval = [0, 16 + 1] # the last token is not part of this modality
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mot_interval = [16 + 1, 2 * 16 + 2]
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vis_state_vector_idx = [app_interval[0], mot_interval[0]]
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response = self.beam_search_generation(
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input_ids,
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app_feat, mot_feat,
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app_interval, mot_interval,
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vis_state_vector_idx, video_place_holder_ids, tokenizer)
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# Decode the response
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response = self.tokenizer.decode(response)
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results[video_name][qid] = response
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time_elapsed = int(time() - start_time)
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print('Generating resonse {} / {} -- took {}s'.format(counter + 1, len(dataset), time_elapsed))
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counter += 1
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# Create a file with all responses
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file_name = 'results_nextqa_beam_depth_{}'.format(self.config['beam_depth'])
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if gen_subset_num is not None:
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file_name += f'-part_{gen_subset_num}'
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file_name = f'{tag}_' + file_name
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output_path = os.path.join(self.config['output_dir_nextqa'], file_name + '.json')
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with open(output_path, 'w') as f:
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json.dump(results, f, indent=4)
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log.info('Results logged to {}'.format(output_path))
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print(os.getcwd())
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# Switch back to training mode
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self.model.train()
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def beam_search_generation(
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self, input_ids,
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app_feat, mot_feat,
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app_interval, mot_interval,
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vis_state_vector_idx, video_place_holder_ids, tokenizer):
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eos_token = tokenizer.eos_token_id
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unk_token = tokenizer.unk_token_id
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question_sep = tokenizer.convert_tokens_to_ids('<s2>')
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gen_ans = [eos_token]
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hyplist = [([], 0.0, [eos_token])]
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best_state = None
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comp_hyplist = []
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app_feat = app_feat.unsqueeze(0).cuda()
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mot_feat = mot_feat.unsqueeze(0).cuda()
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video_place_holder_ids = video_place_holder_ids.cuda()
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text_shift_len = video_place_holder_ids.size(-1)
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question_intervals = [[0 + text_shift_len, input_ids.size(0) + text_shift_len]] # The last token is the question state token (not part of the history)
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question_state_vector_idx = [x[0] for x in question_intervals]
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input_ids = input_ids.long().cuda().unsqueeze(0)
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encoder_outputs = None
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for i in range(self.config['max_generation_length']):
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new_hyplist = []
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argmin = 0
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for out, lp, st in hyplist:
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decoder_input_ids = torch.tensor(st).long().cuda().unsqueeze(0)
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bart_output = self.model(
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input_ids=input_ids,
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video_place_holder_ids=video_place_holder_ids,
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i3d_rgb=app_feat,
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i3d_flow=mot_feat,
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encoder_outputs=encoder_outputs,
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decoder_input_ids=decoder_input_ids,
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i3d_rgb_interval=app_interval,
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i3d_flow_interval=mot_interval,
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question_intervals=question_intervals,
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vis_state_vector_idx=vis_state_vector_idx,
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question_state_vector_idx=question_state_vector_idx,
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output_attentions=True,
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generate=True,
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return_dict=True
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)
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if encoder_outputs is None:
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encoder_outputs = [
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bart_output['encoder_last_hidden_state'],
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bart_output['encoder_hidden_states'],
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bart_output['encoder_attentions'],
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bart_output['encoder_QAs_local'],
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bart_output['encoder_PAs_local'],
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bart_output['encoder_QA_global'],
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bart_output['encoder_PA_global'],
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bart_output['encoder_state_vectors']
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]
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logits = bart_output['logits'][:,-1,:].squeeze() # get the logits of the last token
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logp = F.log_softmax(logits, dim=0)
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lp_vec = logp.cpu().data.numpy() + lp
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if i >= self.config['min_generation_length']:
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new_lp = lp_vec[eos_token] + self.config['length_penalty'] * (len(out) + 1)
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comp_hyplist.append((out, new_lp))
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if best_state is None or best_state < new_lp:
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best_state = new_lp
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count = 1
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for o in np.argsort(lp_vec)[::-1]: # reverse the order
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if o in [eos_token, unk_token]:
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continue
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new_lp = lp_vec[o]
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if len(new_hyplist) == self.config['beam_depth']:
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if new_hyplist[argmin][1] < new_lp:
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new_st = deepcopy(st)
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new_st.append(int(o))
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new_hyplist[argmin] = (out + [o], new_lp, new_st)
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argmin = min(enumerate(new_hyplist), key=lambda h: h[1][1])[0]
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else:
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break
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else:
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new_st = deepcopy(st)
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new_st.append(int(o))
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new_hyplist.append((out + [o], new_lp, new_st))
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if len(new_hyplist) == self.config['beam_depth']:
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argmin = min(enumerate(new_hyplist), key=lambda h: h[1][1])[0]
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count += 1
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hyplist = new_hyplist
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if len(comp_hyplist) > 0:
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maxhyps = sorted(comp_hyplist, key=lambda h: -h[1])[:1]
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return maxhyps[0][0]
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else:
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return []
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